We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.

The impact of disruptions in JET became even more important with the replacement of the previous Carbon Fiber Composite (CFC) wall with a more fragile full metal ITER-like wall (ILW). The development of robust disruption mitigation systems is crucial for JET (and also for ITER). Moreover, a reliable real-time (RT) disruption predictor is a pre-requisite to any mitigation method. The Advance Predictor Of DISruptions (APODIS) has been installed in the JET Real-Time Data Network (RTDN) for the RT recognition of disruptions. The predictor operates with the new ILW but it has been trained only with discharges belonging to campaigns with the CFC wall. 7 real-time signals are used to characterize the plasma status (disruptive or non-disruptive) at regular intervals of 1ms. After the first 3 JET ILW campaigns (991 discharges), the success rate of the predictor is 98.36% (alarms are triggered in average 426ms before the disruptions). The false alarm and missed alarm rates are 0.92% and 1.64%. IntroductIonDue to the complex and highly non-linear coupling of events that lead to a disruption, predictive physics-driven models of these phenomena have not been established from theoretical considerations so far. As an alternative, data-driven models allow the estimation of useful relationships among several quantities to recognize the signature of an incoming disruption.As mentioned in the abstract, the impact of disruptions in JET is a more serious issue with the ILW [1]. This article shows the results of a disruption predictor at JET, APODIS, that has been in operation in the JET RTDN during the three initial ILW campaigns (C28-C30, between August 2011 and July 2012). The objective has been to assess its prediction capabilities (success rate, missed alarms, false alarms and prediction times) for later use in next campaigns as trigger for mitigation actions. In the above ILW campaigns, the alarm generated by APODIS has been distributed through the RTDN and recorded for off-line analysis, but it has not been used to close any feedback loop.

A power-balance model, with radiation losses from impurities and neutrals, gives a unified description of the density limit (DL) of the stellarator, the L-mode tokamak, and the reversed field pinch (RFP). The model predicts a Sudo-like scaling for the stellarator, a Greenwald-like scaling, , for the RFP and the ohmic tokamak, a mixed scaling, , for the additionally heated L-mode tokamak. In a previous paper (Zanca et al 2017 Nucl. Fusion 57 056010) the model was compared with ohmic tokamak, RFP and stellarator experiments. Here, we address the issue of the DL dependence on heating power in the L-mode tokamak. Experimental data from high-density disrupted L-mode discharges performed at JET, as well as in other machines, are taken as a term of comparison. The model fits the observed maximum densities better than the pure Greenwald limit.

The JET 2019-2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major Neutral Beam Injection (NBI) upgrade providing record power in 2019-2020, and tested the technical & procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle physics in the coming D-T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed Shattered Pellet Injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design & operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D-T benefited from the highest D-D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER.

The coupling between a bulk vortical flow and a surfactant-influenced air/water interface has been examined in a canonical flow geometry through experiments and computations. The flow in an annular region bounded by stationary inner and outer cylinders is driven by the constant rotation of the floor and the free surface is initially covered by a uniformly distributed insoluble monolayer. When driven slowly, this geometry is referred to as the deep-channel surface viscometer and the flow is essentially azimuthal. The only interfacial property that affects the flow in this regime is the surface shear viscosity, μs, which is uniform on the surface due to the vanishingly small concentration gradient. However, when operated at higher Reynolds number, secondary flow drives the surfactant film towards the inner cylinder until the Marangoni stress balances the shear stress on the bulk fluid. In general, the flow can be influenced by the surface tension, σ, and the surface dilatational viscosity, κs, as well as μs. However, because of the small capillary number of the present flow, the effects of surface tension gradients dominate the surface viscosities in the radial stress balance, and the effect of μs can only come through the azimuthal stress. Vitamin K1 was chosen for this study since it forms a well-behaved insoluble monolayer on water and μs is essentially zero in the range of concentration on the surface, c, encountered. Thus the effect of Marangoni elasticity on the interfacial stress could be isolated. The flow near the interface was measured in an optical channel using digital particle image velocimetry. Steady axisymmetric flow was observed at the nominal Reynolds number of 8500. A numerical model has been developed using the axisymmetric Navier–Stokes equations to examine the details of the coupling between the bulk and the interface. The nonlinear equation of state, σ(c), for the vitamin K1 monolayer was measured and utilized in the computations. Agreement was demonstrated between the measurements and computations, but the flow is critically dependent on the nonlinear equation of state.